Modeling and simulation are used to solve real-world problems safely and efficiently. For example, we can conduct simulations and analyze the results by constructing digital models of cyber-physical systems to make informed decisions. The field of Modeling and Simulation has recently grown and is tackling systems with increased complexity and size. Hence, modeling and simulating cyber-physical systems are becoming increasingly complex, requiring traditional modeling languages and tools to adapt. Modelica is an open-standard declarative equation-based object-oriented language used to model various systems. Existing tools allow modelers to model and simulate large, complex systems. However, the language and existing tooling cannot express some concepts, such as radical structural changes to the components or the behavior of systems during simulation. We propose several language extensions to support modeling variable-structure systems (VSS), that is, systems in which the system’s structure may radically change during simulation. To achieve these goals, we have developed a new modeling framework that supports the language itself alongside several extensions. The proposed extensions can handle explicit and implicit modeling of VSS by new operators and, consequently, new semantics for the language. Furthermore, we introduce and investigate additional features in terms of operators and semantics to aid VSS modeling and simulation.

One such extension is dynamic-overconstrained connectors (DOCC), which are helpful, for instance, when modeling AC transmission systems, particularly large ones. In the thesis we provide a technique to better handle such models by relaxing existing modeling constraints. Another extension is the inclusion of a experimental operator for inline integration and investigating the practical benefits of its introduction. The environment has been used to develop a new language that merges aspects of Context-Oriented Programming and declarative equation-based modeling, utilizing the introduced primitives. Furthermore, we have also validated the framework by using it to simulate relvant and recent climate models.

Explicit VSS modeling is based on extensions, which provide the possibility of switching configuration at runtime between model definitions resolved at compile time. The implicit modeling supports reconfiguration during runtime via recompilation. A just-in-time compiler was implemented to handle the new semantics using the symbolic-numeric programming language Julia. We investigate the performance of our new framework and compare it to existing state-of-the-art tooling on models with over 100,000 equations.

The results show that the extensions, framework and methods are viable for simulating both regular models and models with structural variability. We demonstrate that the Modelica language can be extended to support systems with variable structures by providing additional operators and enhanced runtime system support.

As a result of frequency and power limitations, multi-core processors and accelerators are becoming more and more prevalent in today's systems. To fully utilize such systems, heterogeneous parallel programming is needed, but this introduces new complexities to the development. High-level frameworks such as SkePU have been introduced to help alleviate these complexities. SkePU is a skeleton programming framework based on a set of programming constructs implementing computational parallel patterns, while presenting a sequential interface to the programmer. Using the various skeleton backends, SkePU programs can execute, without source code modification, on multiple types of hardware such as CPUs, GPUs, and clusters. This paper presents the design and implementation of a new backend for SkePU, adding support for FPGAs. We also evaluate the effect of FPGA-specific optimizations in the new backend and compare it with the existing GPU backend, where the actual devices used are of similar vintage and price point. For simple examples, we find that the FPGA-backend's performance is similar to that of the existing backend for GPUs, while it falls behind in more complex tasks. Finally, some shortcomings in the backend are highlighted and discussed, along with potential solutions.

Modeling and Simulation are usually used to solve real-world problems safely and efficiently by constructing digital models of Cyber-Physical Systems. The models can be simulated and analyzed with respect to requirements, and decisions about their design can be based on this analysis. In the latest years, the field of Modeling and Simulation has grown massively and is tackling systems with increased complexity. Thus, the process of modeling and simulating Cyber-Physical systems is becoming more and more complex. This increase requires modeling languages that can express systems with increasing complexity.

Modelica is an open-standard declarative equation-based object-oriented language used to model various systems expressed using equations. Modelica tools can read the models, process them, and simulate them. However, the Modelica language and tools cannot express some concepts such as structural changes to the components or behavior of Cyber-Physical Systems during Simulation.

In this thesis, we propose extensions of the Modelica language to support modeling so-called variable structure systems, that is, systems where the structure of the system varies during Simulation. The full Modelica language and the new extensions are supported by a novel composable programming environment framework called OpenModelica.jl written in the Julia language. The proposed Modelica language extensions can handle explicit and implicit modeling of variable structure systems by introducing new operators and, consequently, new semantics to the Modelica language.

The explicit modeling is based on extensions that switch at runtime between continuous modes of operations with operators similar to the ones used in the specification of Modelica state-machines. The implicit modeling supports reconfiguration during runtime via recompilation. A Just-in-time compiler was implemented to handle the new semantics using the symbolic-numeric programming language Julia.

We investigate the performance of our new framework and compare it with existing state-of-the-art Modelica tools on models with thousands of equations and variables. The results show that our extensions and proposed runtime framework is viable for simulating both usual Modelica models and models with variable structure systems.

The conclusion is that the Modelica language can be extended further to support systems with variable structures with the addition of a few operators and JIT enhanced runtime system support. Based on the result of this thesis, we propose several directions for future work.

Nowadays, industrial products are getting increasingly complex, and time-to-market is significantly shorter. Modeling and simulation tools for cyber-physical systems need to keep up with the increased complexity. This paper presents OpenModelica.jl, a modular and extensible Modelica compiler framework in Julia targeting ModelingToolkit.jl and supporting Variable Structured Systems. We extended the Modelica language with three new operators to support continuous-time mode-switching and reconfiguration via recompilation at runtime. Therefore, our compiler supports the Modelica language and variable structure systems via the aforementioned extensions. To our knowledge, there are no other Modelica tools available that support both standard Modelica and variable structure systems. We evaluated our framework using a standardized benchmark suite, in terms of simulation, compilation and recompilation performance. The results concerning compilation and simulation time performance were compared with the results of running the existing OpenModelica compiler with the same set of models. A custom benchmark was devised to estimate the cost in terms of recompilation when simulating variable structure systems. The performance experiments showed that OpenModelica.jl is currently about four times slower in terms of compilation time when compiling a transmission line model with tens of thousands of equations and variables. The difference in simulation performance between the two compilers was negligable. Furthermore, the impact of recompilation during the simulation was usually small compared with the simulation time for long simulations. The results are promising for a prototype, and we outline approaches to further improve both compilation and simulation performance as future research. 

Cyber-Physical Systems are ever-increasing in complexity and new methods and tools for developing them are needed. To support these highly dynamic systems, increasing the flexibility of the modeling languages is desirable. This paper proposes and examines a Modelica language extension to support dynamic overconstrained graphs with reconfiguration at runtime. Two applications of this new feature are also discussed: synchronous AC power systems and incompressible fluid networks. Reported findings suggest that supporting dynamic overconstrained graphs might yield performance benefits and provide the possibility of simulating systems that can not currently be simulated in existing Modelica tools.

OpenModelica is a unique large-scale integrated open-source Modelica- and FMI-based modeling, simulation, optimization, model-based analysis and development environment. Moreover, the OpenModelica environment provides a number of facilities such as debugging; optimization; visualization and 3D animation; web-based model editing and simulation; scripting from Modelica, Python, Julia, and Matlab; efficient simulation and co-simulation of FMI-based models; compilation for embedded systems; Modelica-UML integration; requirement verification; and generation of parallel code for multi-core architectures. The environment is based on the equation-based object-oriented Modelica language and currently uses the MetaModelica extended version of Modelica for its model compiler implementation. This overview paper gives an up-to-date description of the capabilities of the system, short overviews of used open source symbolic and numeric algorithms with pointers to published literature, tool integration aspects, some lessons learned, and the main vision behind its development.

Recently the Julia language has become an option for scientific computing. As of 2020, efforts exist to provide libraries that emulate the equation-based modeling features provided by Modelica or otherwise provide such functionality in Julia. The issue with these approaches is that investment in standardization and libraries would be lost unless standard-complacency is guaranteed. We believe that it is possible to combine features from both by implementing such a compiler in Julia. We argue that this approach would open additional opportunities. One such being the handling of variable structure systems (VSS) within the framework of a Modelica standard-compliant compiler. The other being a proposed compiler architecture reminiscent of LLVM for equation-based object-oriented languages. Using the OpenModelica Compiler as a baseline, we verified the fidelity of our implementation by simulating a selected set of models. While there are performance penalties, we argue that improvements to the frontend would mitigate these issues.

We are investigating ways of introducing just-in-time compilation in a standard-compliant Modelica compiler, the Open-Modelica compiler (OMC). The main motivations are enabling extensions to support dynamically varying model structure, faster compilation, and faster recompilation of models after changes. We are investigating two approaches.

The first approach is to adapt the low-level OpenModelica intermediate representation (IR) before code generation to be compatible with LLVM. In that way we can avoid generating intermediate C-code and instead generate LLVM IR in memory for just-in-time compilation (JIT).

The second approach is to translate OMC itself written in MetaModelica to Julia, and thereby gain access to the JIT capabilities of LLVM. Another benefit of the second approach is the access to the Julia ecosystem, including a rich set of libraries for numerical computing.

We have done a preliminary investigation of both approaches, with measurements on a selected sample of algorithms, and discovered that compilation-time of generated Julia code is slower compared to generating LLVM IR directly. We conclude that providing a standard-compliant Modelica compiler which supports a dynamically varying model structure is feasible and possible, and we believe that such a compiler can be provided by using Julia or MetaModelica.